Dr. Justin Dauwels

School of Electrical and Electronic Engineering Nanyang Technological University, Singapore

October 14, 2016, 12:15, Room GC A3 31 (click here for the map)

Data analytics and simulation tools for urban mobility of the future

An estimated 64% of all travel today is made within urban environments. By 2050 the total amount of urban kilometres travelled worldwide is expected to triple, with traffic congestion potentially bringing major cities to a standstill. In Singapore, a small island with a population of 5.4 million, there are approximately 1 million cars on the roads. At the same time, roads take up 12% of land space. With the limited land space in Singapore, it is unrealistic to further increase the number of vehicles or add more roads. To address these challenges, the Singapore government plans to implement an intelligent and adaptable transport system which uses data to empower commuters and adjusts to their needs. Sensor networks are being deployed that collect data from busy areas such as traffic junctions, bus stops and taxi queues, then relay it back to the relevant agencies for analysis through data analytics and real-world applications. Besides transportation systems powered by big data analytics, driverless vehicles are also a major focus so far for the Singapore government. More than six kilometres of public roads have been opened this year for AV trials, currently in use for trials with a small fleet of public self-driving taxis. Various stakeholders are aiming for full-scale commercial autonomous taxi service in 2018 in Singapore. It is within this context that our research group has developed various data analytics and simulation tools for transportation applications. In the seminar, I will give an overview of our research efforts. Over the last years, we have been working towards scalable real-time algorithms for predicting traffic speed and travel time. The prediction systems designed by our team is able to perform accurate real-time predictions in large networks consisting of 10,000 - 100,000 links, by exploiting the correlations in traffic data. The sensing and prediction can be performed in a distributed fashion, e.g., on smartphones, as alternative to high-cost centralized systems. In recent work, we are investigating the effect of rainfall and road incidents on road traffic, in an attempt to further improve traffic predictions by incorporating information about traffic incidents and weather. We are also working towards traffic-and weather-aware online stochastic routing algorithms that are able to adapt the routes of vehicles based on real-time information about the condition of the transportation networks. Besides macro-scale data analytics, our team is designing machine learning algorithms for micro-scale transportation applications. Specifically, currently we are creating algorithms for scene understanding in urban and off-road scenarios. In collaboration with our local industry partner ST Engineering, we are integrating these technologies into autonomous vehicles (AVs) for urban mobility and airport automation. In parallel efforts, we have created a simulation platform for exploring emerging transportation paradigms. One of these technologies is vehicle-to-vehicle (V2V) and vehicle-to-infrastructure communications systems (V2X). Our simulation platform allows researchers to explore various use cases of V2V/V2X technologies at a high level of realism, including smart traffic signals and vehicle platooning. As part of the recently established Centre of Excellence for Testing and Research of Autonomous Vehicles - NTU (CENTRAN), the team is currently incorporating realistic models of AVs into the simulation platform, which will yield a sophisticated simulation tool for studying and testing AVs and designing the required infrastructure for supporting AVs. This simulation tool will be instrumental for the certification of AVs to be deployed in Singapore. The tool will also allow us to simulate and design various approaches to collect, communicate, and analyse transportation data through networks of V2V/V2X enabled AVs, providing real-time macro-scale analytics about transportation networks.


Dr. Justin Dauwels is an Associate Professor with School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore. He serves as Deputy Director of the ST Engineering NTU corporate lab, which comprises 100+ PhD students, research staff and engineers, developing novel autonomous systems for airport operations and transportation. He is also involved as project PI in the Centre of Excellence for Testing and Research of Autonomous Vehicles - NTU (CENTRAN), which will lead the development of testing requirements for such vehicles, and was launched by the Land Transport Authority (LTA) and JTC, in partnership with NTU. Moreover, he serves as project PI in the BMW-NTU lab on Future Mobility, and the NXP-NTU lab on vehicle-to-vehicle communications. His research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behavior and physiology. He obtained the PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. He was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010). He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011). His research on intelligent transportation systems has been featured by the BBC, Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. His research team has won several best paper awards at international conferences. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation and logistics.